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torchrun --standalone --nnodes=1 --nproc_per_node=1 run_speech_recognition_seq2seq_streaming.py \
--model_name_or_path="openai/whisper-large-v2" \
--dataset_name="audiofolder" \
--dataset_data_dir="../data/dataset_wav" \
--language="finnish" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--model_index_name="Whisper Large Sámi" \
--output_dir="./" \
--max_steps="60000" \
--per_device_train_batch_size="12" \
--per_device_eval_batch_size="6" \
--gradient_accumulation_steps="1" \
--logging_steps="25" \
--learning_rate="1e-5" \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="225" \
--length_column_name="input_length" \
--max_duration_in_seconds="30" \
--text_column_name="transcription" \
--freeze_feature_encoder="False" \
--report_to="tensorboard" \
--metric_for_best_model="wer" \
--greater_is_better="False" \
--load_best_model_at_end \
--gradient_checkpointing="true" \
--fp16="true" \
--do_train \
--do_eval \
--predict_with_generate \
--do_normalize_eval \
--use_auth_token \
--overwrite_output_dir \
--push_to_hub_organization="NbAiLab" \
--push_to_hub_model_id="whisper-large-sme" \
--push_to_hub
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